* [Gemma 3 Technical Report][g3-tech-report] * [Responsible Generative AI Toolkit][rai-toolkit] * [Gemma on Kaggle][kaggle-gemma] * [Gemma on Vertex Model Garden][vertex-mg-gemma3] Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. Gemma 3 models are multimodal, handling text and image input and generating text output, with open weights for both pre-trained variants and instruction-tuned variants. Gemma 3 has a large, 128K context window, multilingual support in over 140 languages, and is available in more sizes than previous versions. Gemma 3 models are well-suited for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as laptops, desktops or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone. - **Input:** - Text string, such as a question, a prompt, or a document to be summarized - Images, normalized to 896 x 896 resolution and encoded to 256 tokens each - Total input context of 128K tokens for the 4B, 12B, and 27B sizes, and 32K tokens for the 1B size

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gemma-3-27b-lenientchatfix

m8than

See our collection for all versions of Gemma 3 including GGUF, 4-bit & 16-bit formats. - Fine-tune Gemma 3 (12B) for free using our Google Colab notebook here! - Read our Blog about Gemma 3 support: unsloth.ai/blog/gemma3 - View the rest of our notebooks in our docs here. - Export your fine-tuned model to GGUF, Ollama, llama.cpp or 🤗HF. * [Gemma 3 Technical Report][g3-tech-report] * [Responsible Generative AI Toolkit][rai-toolkit] * [Gemma on Kaggle][kaggle-gemma] * [Gemma on Vertex Model Garden][vertex-mg-gemma3]

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GLM-5.2-E2M1-Furnace

m8than

Complete experimental conversion of zai-org/GLM-5.2-FP8 for Furnace. **142 safetensors shards, 118,629 tensors, approximately 388 GB.** The source checkpoint is unchanged. This is a full target model, not a pilot or draft. Routed experts are E2M1 (4-bit, block-128 scales), as before. Attention, dense and shared-expert projections are now **FP8 block-128, passed through unchanged from the source** (`dense_weight_format: fp8_block128` in `quantization_config`), instead of BF16. Expert weights and their reconstruction error are identical to the previous revision. The previous BF16-dense revision is `16c3f5ca406349306ffe69de7726d7156b09b91b`. This needs a Furnace build that understands `dense_weight_format` (the commit linked above or later). FP8 dense weights let Furnace's `FURNACE_MEGAKERNEL=1` decode path qualify on this checkpoint.

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GLM-5.3-E2M1-Furnace

m8than

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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KimiK3-GG

m8than

This checkpoint is derived from Kimi K3 by Moonshot AI. The original LICENSE and any accompanying notices govern use and are retained. This is a converted, additionally quantized checkpoint, not the original model. It requires a compatible runtime supporting the quantization format specified in config.json. That required metadata is public and must be retained; standard model loaders are not necessarily compatible with this checkpoint. Conversion can change model behavior. Full-model accuracy, perplexity, and generation quality are not certified. Evaluate quality and compatibility for your intended use before deployment.

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